Background: Atherosclerotic disease is the leading global cause of death, driven by progressive plaque accumulation in the arteries. Ultrasound (US) imaging, both conventional (CUS) and intravascular (IVUS), is crucial for the non-invasive assessment of atherosclerotic plaques. Deep learning (DL) techniques have recently gained attention as tools to improve the accuracy and efficiency of image analysis in this domain. This paper reviews recent advancements in DL-based methods for the segmentation, classification, and quantification of atherosclerotic plaques in US imaging, focusing on their performance, clinical relevance, and translational challenges. Methods: A systematic literature search was conducted in the PubMed, Scopus, and Web of Science databases, following PRISMA guidelines. The review included peer-reviewed original articles published up to 31 January 2025 that applied DL models for plaque segmentation, characterization, and/or quantification in US images. Results: A total of 53 studies were included, with 72% focusing on carotid CUS and 28% on coronary IVUS. DL architectures, such as UNet and attention-based networks, were commonly used, achieving high segmentation accuracy with average Dice similarity coefficients of around 84%. Many models provided reliable quantitative outputs (such as total plaque area, plaque burden, and stenosis severity index) with correlation coefficients often exceeding R = 0.9 compared to manual annotations. Limitations included the scarcity of large, annotated, and publicly available datasets; the lack of external validation; and the limited availability of open-source code. Conclusions: DL-based approaches show considerable promise for advancing atherosclerotic plaque analysis in US imaging. To facilitate broader clinical adoption, future research should prioritize methodological standardization, external validation, data and code sharing, and integrating 3D US technologies.